Skip to main content
SpringerLink
Log in

A Ship Detection Method in Complex Background Via Mixed Attention Model

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

With the development of deep learning, recent object detection methods have made considerable progress. Unlike other simple visual object detection problems, it is more difficult to detect ships in a complex background. Nearshore vessels are always confused with background objects. When the ship target is small, the feature information in the background will greatly interfere with the information extraction and learning of the target area of the ship by the convolution neural network, resulting in the problem of ship sample imbalance. In response to this problem, this paper proposes a mixed attention model to decrease the difficulty of ship detection, which is composed of pixel attention model (PAM) and feature attention model (FAM). The PAM structure is a generative adversarial network designed to prove the sensitivity of the target area without extra manual works. FAM is a convolution network designed to increase the utilization rate of useful features. While MAM is not a fixed structure, it could be implanted into almost any object detection and classification networks. Meanwhile, PAM is for the original image preprocessing part and FAM is always added to the position of low-level features to high-level features. Following experimental results show that using MAM method into Yolov3, the mean average precision increased by 2.2% when achieved 0.975, which effectively improve the accuracy of ship detection in complex background.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Sasikala, J.: Ship detection and recognition for offshore and inshore applications: a survey[J]. Int. J. Intell. Unmanned Syst. (2019). https://doi.org/10.1108/IJIUS-04-2019-0027

    Article  Google Scholar 

  2. He, H.; Lin, Y.; Chen, F., et al.: Inshore ship detection in remote sensing images via weighted pose voting[J]. IEEE Trans. Geoence Remote Sens. 55, 3091–3107 (2017)

    Article  Google Scholar 

  3. Zhang, L.; Hong, X.; Wang, Y.H., et al.: Inshore ship detection in high-resolution remote sensing image using projection analysis [J][J]. J. Image Graph. 23(9), 1424–1432 (2018)

    Google Scholar 

  4. Zhai, L.; Li, Y.; Su, Y.: Inshore ship detection via saliency and context information in high-resolution SAR images[J]. IEEE Geosci. Remote Sens. Lett. 13, 1870–1874 (2016)

    Article  Google Scholar 

  5. Li, Y.; Zhang, X.; Li, H., et al.: Object detection and tracking under complex environment using deep learning-based LPM[J]. IET Comput. Vision 13(2), 157–164 (2019)

    Article  Google Scholar 

  6. Song, P.; Qi, L.; Qian, X., et al.: Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models[J]. J. Parallel Distrib. Comput. 132, 1–7 (2019)

    Article  Google Scholar 

  7. Zhang, T.; Hao, L.Y.; Guo, G.: A feature enriching object detection framework with weak segmentation loss[J]. Neurocomputing 335, 72–80 (2019)

    Article  Google Scholar 

  8. Gallego, A.J.; Pertusa, A.; Gil, P.: Automatic ship classification from optical aerial images with convolutional neural networks[J]. Remote Sens. 10(4), 511 (2018)

    Article  Google Scholar 

  9. Krizhevsky, A.; Sutskever, I.; Hinton, G.E.: ImageNet classification with deep convolutional neural networks[J]. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  10. Long, J.; Shelhamer, E.; Darrell, T.: Fully convolutional networks for semantic segmentation[C]/Proceedings of the IEEE conference on computer vision and pattern recognition. 3431–3440 (2015)

  11. Tang, H.; Liu, H.; Xu, D. et al.: Attentiongan: unpaired image-to-image translation using attention-guided generative adversarial networks[J]. arXiv preprint, (2019)

  12. Feng, T.; Gu, D.: SGANVO: unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks[J]. IEEE Robot. Autom. Lett. 4(4), 4431–4437 (2019)

    Article  Google Scholar 

  13. Ma, J.; Zhou, Z.; Wang, B., et al.: Ship detection in optical satellite images via directional bounding boxes based on ship center and orientation prediction[J]. Remote Sens. 11(18), 2173 (2019)

    Article  Google Scholar 

  14. Yao, Y.; Jiang, Z.; Zhang, H., et al.: Ship detection in optical remote sensing images based on deep convolutional neural networks[J]. J. Appl. Remote Sens. 11(4), 1 (2017)

    Article  Google Scholar 

  15. Huang, G.; Wan, Z.; Liu, X., et al.: Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images[J]. Neurocomputing 332, 215–223 (2019)

    Article  Google Scholar 

  16. Guo, M.; Guo, C.; Zhang, C., et al.: Fusion of ship perceptual information for electronic navigational chart and radar images based on deep learning[J]. J. Navig. 73(1), 192–211 (2020)

    Article  Google Scholar 

  17. You, Y.; Cao, J.; Zhang, Y., et al.: Nearshore ship detection on high-resolution remote sensing image via scene-Mask R-CNN[J]. IEEE Access 7, 128431–128444 (2019)

    Article  Google Scholar 

  18. Han, J.; Yu, Y.; Liang, K., et al.: Infrared small-target detection under complex background based on subblock-level ratio-difference joint local contrast measure[J]. Opt. Eng. 57(10), 103105 (2018)

    Article  Google Scholar 

  19. Ng, W.W.Y.; Hu, J.; Yeung, D.S., et al.: Diversified sensitivity-based undersampling for imbalance classification problems[J]. IEEE Trans. Cybern. 45(11), 2402–2412 (2017)

    Article  Google Scholar 

  20. Zhuang, Y.; Li, L.; Chen, H.: Small sample set inshore ship detection from VHR optical remote sensing images based on structured sparse representation[J]. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2145–2160 (2020)

    Article  Google Scholar 

  21. Deng, Z.; Sun, H.; Zhou, S., et al.: Learning deep ship detector in SAR images from scratch[J]. IEEE Trans. Geosci. Remote Sens. 57(6), 4021–4039 (2019)

    Article  Google Scholar 

  22. Zhou, M.; Jing, M.; Liu, D., et al.: Multi-resolution networks for ship detection in infrared remote sensing images[J]. Infrared Phys. Technol. 92, 183–189 (2018)

    Article  Google Scholar 

  23. Buda, M.; Maki, A.; Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks[J]. Neural Netw. 106, 249–259 (2018)

    Article  Google Scholar 

  24. Pang, J.; Chen, K.; Shi, J.; et al.: Libra r-cnn: towards balanced learning for object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 821–830 (2019)

  25. Gao, L.; He, Y.; Sun, X., et al.: Incorporating negative sample training for ship detection based on deep learning[J]. Sensors 19(3), 684 (2019)

    Article  Google Scholar 

  26. Derakhshani, M.M.; Masoudnia, S.; Shaker; A.H. et al.: Assisted excitation of activations: a learning technique to improve object detectors[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 9201–9210, (2019)

  27. Zhou, X.; Zhuo, J.; Krahenbuhl, P.: Bottom-up object detection by grouping extreme and center points[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 850–859 (2019)

  28. Li, X.; Wang, S.: Object detection using convolutional neural networks in a coarse-to-fine manner[J]. IEEE Geosci. Remote Sens. Lett. 14(11), 2037–2041 (2017)

    Article  Google Scholar 

  29. Redmon, J.; Divvala, S.; Girshick, R. et al.: You only look once: unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 779–788 (2016)

  30. Redmon, J.; Farhadi, A.: YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 7263–7271 (2017)

  31. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement[J]. arXiv preprint, (2018)

  32. Lee, H.; Eum, S.; Kwon, H.: Me r-cnn: multi-expert r-cnn for object detection[J]. IEEE Trans. Image Process. 29, 1030–1044 (2019)

    Article  MathSciNet  Google Scholar 

  33. Girshick, R.: Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision, 1440–1448 (2015)

  34. Ren, S.; He, K.; Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  35. He, K., Gkioxari, G., Dollár, P. et al.: Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2961–2969 (2017)

  36. Chang, Y.L.; Anagaw, A.; Chang, L., et al.: Ship detection based on YOLOv2 for SAR imagery[J]. Remote Sens. 11(7), 786 (2019)

    Article  Google Scholar 

  37. Yang, X.; Sun, H.; Fu, K., et al.: Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks[J]. Remote Sens. 10(1), 132 (2018)

    Article  Google Scholar 

  38. Zhang, R.; Yao, J.; Zhang, K., et al.: S-CNN-based ship detection from high-resolution remote sensing images[J].(2016). https://doi.org/10.5194/isprsarchives-XLI-B7-423-2016

  39. Iglovikov, V.; Shvets, A.; Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation[J]. arXiv preprint, (2018)

  40. Lin, D.Y.: Deep unsupervised representation learning for remote sensing images[J]. IEEE Geosci. Remote Sens. Lett. 14(11), 2092–2096 (2016)

    Article  Google Scholar 

  41. Mehralian, M.; Karasfi, B.: RDCGAN: unsupervised representation learning with regularized deep convolutional generative adversarial networks[C]//2018 9th conference on artificial intelligence and robotics and 2nd Asia-Pacific international symposium. IEEE, 31–38, (2018)

  42. Deng, Y.; Wang, H.; Liu, S., et al.: Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection[J]. EURASIP J. Wirel. Commun. Netw. 2018(1), 1–9 (2018)

    Article  Google Scholar 

  43. Shao, Z.; Wu, W.; Wang, Z., et al.: Seaships: a large-scale precisely annotated dataset for ship detection[J]. IEEE Trans. Multimedia 20(10), 2593–2604 (2018)

    Article  Google Scholar 

  44. Zhu, J.Y.; Park, T.; Isola, P. et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE international conference on computer vision. 2223–2232 (2017)

Download references

Acknowledgements

The Project of Intelligent Situation Awareness System for Smart Ship (Grant: MC-201920-X01)

Author information

Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Hao Meng, Fei Yuan, Yang Tian & Hongwei Wei

Authors
  1. Hao Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongwei Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei Yuan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, H., Yuan, F., Tian, Y. et al. A Ship Detection Method in Complex Background Via Mixed Attention Model. Arab J Sci Eng 47, 9505–9525 (2022). https://doi.org/10.1007/s13369-021-06275-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06275-2

Keywords

Search

Navigation